Guided post-earthquake reconnaissance surveys considering resource constraints for regional damage inference
Guided post-earthquake reconnaissance surveys considering resource constraints for regional damage inference
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DOI：
10.1177/87552930221101415
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发表时间：
2022-06
影响因子：
5
通讯作者：
M. Sheibani;Ge Ou
中科院分区：
文献类型：
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作者：
M. Sheibani;Ge Ou
地震灾害的损失程度可以通过及时分配资金和启动恢复任务来减轻。在灾害发生后进行的各种检查中，建筑物的损坏情况被评估为勘察的一部分，以了解和记录地震对建筑物的影响。调查的结果被用于财政援助的估计，这是至关重要的社区快速恢复灾害后的行动。由于对这些信息的迫切需要，单位时间内获得的信息量应得到优化。本文旨在回答如何在资源有限的情况下最大限度地提高信息增益的问题，指导侦察测量队的努力。提出了一种数据驱动的方法，该方法可以主动学习损坏模式，并在考虑资源限制的情况下推荐信息最丰富的建筑物进行检查。该框架利用一种有效的主动学习方法的基础上互信息和高斯过程回归（GPR），以确定信息丰富的情况下。为了评估信息增益和资源分配的整体结果的损害推断的贡献，两个模拟地震试验台进行了研究。结果表明，在一个共同优化的方法，大多数建筑物的损坏标签可以准确地预测后1周的损坏检查。
The extent of loss in a seismic hazard can be moderated with on-time allocation of funds and initiation of recovery tasks. Among various examinations conducted following the hazard, buildings damages are assessed as part of the reconnaissance survey to learn and document the impact of the earthquake on structures. The results of the survey are used in financial aid estimation, which is crucial for the community rapid recovery acts after the hazard. Due to the urgent need for this information, the amount of information gained per unit of time should be optimized. This article aims at answering the question of how to maximize the information gain in the presence of resource constraints by directing the efforts of a reconnaissance surveying team. A data-driven method is proposed that actively learns the patterns of damage and recommends the most informative buildings to be inspected while considering the resource limitations. The framework utilizes an efficient active learning method based on mutual information and developed for Gaussian process regression (GPR) to identify the information-rich cases. To assess the contribution of information gain and resource allocation in the overall outcome of the damage inference, two simulated earthquake testbeds are studied. It is shown that in a co-optimization approach, damage labels of the majority of buildings can be accurately predicted after 1 week of damage inspections.